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# More elegant way to generate adjacency matrix from list of tuples

By : Adam Ušela
Date : October 16 2020, 06:10 AM
I wish this help you My algorithm would be something like this:
Find the maximum vertex id. Call this n. Create an n+1 by n+1 array of zeros. Call this M. For each pair x, y in the input list, set M[x][y] = 1
code :

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## Adjacency Matrix of Non-numeric tuples

By : Mahesh Bahute
Date : March 29 2020, 07:55 AM
it helps some times You really just need to get the labels for the rows and columns. From there, it's just a few for loops:
code :
``````from __future__ import print_function

import itertools

reservoir = {
('a', 'b'): 2,
('a', 'c'): 3,
('b', 'a'): 1,
('b', 'c'): 3,
('c', 'a'): 1,
('c', 'b'): 2,
('c', 'd'): 5
}

fields = sorted(list(set(itertools.chain.from_iterable(reservoir))))

print(' ', *fields)

for row in fields:
print(row, end=' ')

for column in fields:
print(reservoir.get((row, column), 0), end=' ')

print()
``````

## igraph generate adjacency matrix from adjacency list

By : user2323077
Date : March 29 2020, 07:55 AM
With these it helps You misunderstood the commands. The command get.adjlist takes as parameter an igraph graph object, and returns a list-type object representation of the graph. You are applying this to a data frame which is not being coerced to an igraph object.
Below is the correct way to construct a igraph graph object using a data frame, and how to get various graph representations of this object.
code :
``````require(reshape2)
net_list <- melt( net_test, id.vars = "id")
net_list <- net_list[ !is.na(net_list\$value), c("id", "value") ]
graph_o <- graph.data.frame(net_list) #This is a proper igraph graph object
#got from a data frame directly

list_rep <- get.adjlist(graph_o) #this now returns an adjacency list
#representation of your graph
matrix_rep <- get.adjacency(graph_o) #this gives you the adjacency
#matrix as a (sparse) matrix with the row and column names as you want.
``````

## Is the complexity of prim's MST algorithm by adjacency matrix same as that of adjacency list with linear search?

By : Bas Vegter
Date : March 29 2020, 07:55 AM
Does that help You're right; there's no point in using a fancy heap data structure for Prim on dense graphs. The defining feature of Prim, though, is not the heap so much as the idea of repeatedly extending a partial MST as cheaply as possible.
The point of an adjacency matrix is for space (no pointer overhead) and architectural reasons (sequential accesses are typically much more efficient than random).

## Create adjacency matrix from nearest neighbour search. (convert adjacency list to adjacency matrix) - Matlab

By : shwetaqwe
Date : March 29 2020, 07:55 AM
Hope that helps I have a matrix 2000x5, in the first column the point number, and in columns 2-5 the 4 neighbours (0s if there isnt a neighbour). Is there an efficient way to create an adjacency matrix out of this ? , A quick and simple technique:
code :
``````adjMat = zeros(size(A,1));
for ind = 1:size(A,1)
% Flag 1 on each row 'ind' at the indices mentioned in col 2-5
adjMat(ind, nonzeros(A(ind,2:end))) = 1;
end
``````
``````A = [1 2 3; 2 0 1; 3 1 4; 4 5 3; 5 4 0]

A =

1     2     3
2     0     1
3     1     4
4     5     3
5     4     0
``````
``````adjMat =

0     1     1     0     0
1     0     0     0     0
1     0     0     1     0
0     0     1     0     1
0     0     0     1     0
``````
``````adjMat(nonzeros(A(ind,2:end)),ind) = 1;
``````

## Generate adjacency matrix from a list, where adjacency means equal elements

By : Svitlana Rytkina
Date : March 29 2020, 07:55 AM
will help you Use broadcasted comparison -
code :
``````np.equal.outer(lst, lst).astype(int) # or convert to float
``````
``````In [787]: lst = [0, 1, 0, 5, 0, 1]

In [788]: np.equal.outer(lst, lst).astype(int)
Out[788]:
array([[1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 1],
[1, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 0],
[1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 1]])
``````
``````In [793]: a = np.asarray(lst)

In [794]: (a[:,None]==a).astype(int)
Out[794]:
array([[1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 1],
[1, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 0],
[1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 1]])
``````
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